Multiple imputations for missing data in lifecourse studies

Missing imputation (MI) is a method to deal with missing at random (MAR) data. It is a Monte Carlo procedure where missing values are replaced by several (usually less than 10) simulated versions. It consists of three steps (Shafer, 1999): i. generation of the imputed values for the missing data; ii. analysis of each imputed data set where missing observations are replaced by imputed ones; iii. combination of the results from all imputed data sets. The procedure is easily implemented in Stata for univariate normally distributed missing variables. Extensions to the case of multivariate normal variables - often encountered in life course epidemiology - will be discussed.

Detecting bias arising from delayed recording of time

Sometimes in studies of the dependence of survival time on explanatory variables the natural time origin for defining entry into study cannot be observed and a delayed time origin is used instead. For example, diagnosis of disease may in some patients be made only at death. The effect of such delays is investigated both theoretically and in the context of the England and Wales National Cancer Register

Years of sunlight exposure and cataract: a case-control study in a Mediterranean population.

BACKGROUND: We aimed to investigate the relation between sunlight exposure and risk of cataract. METHODS: We carried out a frequency-matched case-control study of 343 cases and 334 controls attending an ophthalmology outpatient clinic at a primary health-care center in a small town near Valencia, Spain. All cases were diagnosed as having a cataract in at least one eye based on the Lens Opacification Classification system (LOCS II). Controls had no opacities in either eye. All cases and controls were interviewed for information on outdoor exposure, "usual" diet, history of severe episodes of diarrhea illness, life-style factors and medical and socio-demographic variables. Blood antioxidant vitamin levels were also analyzed. We used logistic regression models to estimate sex and age-adjusted odds ratios (ORs) by quintiles of years of occupational outdoor exposure, adjusting for potential confounders such as smoking, alcohol consumption, serum antioxidants and education. RESULTS: No association was found between years of outdoor exposure and risk of cataract. However, exploratory analyses suggested a positive association between years of outdoor exposure at younger ages and risk of nuclear cataract later in life. CONCLUSION: Our study does not support an association with cataract and sunlight exposure over adult life

Levels of disability in the older population of England: Comparing binary and ordinal classifications.

BACKGROUND: Recent studies suggest the importance of distinguishing severity levels of disability. Nevertheless, there is not yet a consensus with regards to an optimal classification. OBJECTIVE: Our study seeks to advance the existing binary definitions towards categorical/ordinal manifestations of disability. METHODS: We define disability according to the WHO's International Classification of Functioning, Disability and Health (ICF) using data collected at the baseline wave of the English Longitudinal Study of Aging, a longitudinal study of the non-institutionalized population, living in England. First, we identify cut-off points in the continuous disability score derived from ICF to distinguish disabled from no-disabled participants. Then, we fit latent class models to the same data to find the optimal number of disability classes according to: (i) model fit indicators; (ii) estimated probabilities of each disability item; (iii) association of the predicted disability classes with observed health and mortality. RESULTS: According to the binary classification criteria, about 32% of both men and women are classified disabled. No optimal number of classes emerged from the latent class models according to model fit indicators. However, the other two criteria suggest that the best-fitting model of disability severity has four classes. CONCLUSIONS: Our findings contribute to the debate on the usefulness and relevance of adopting a finer categorization of disability, by showing that binary indicators of disability averaged the burden of disability and masked the very strong effect experienced by individuals having severe disability, and were not informative for low levels of disability